Device handoff identification proofing using behavioral analytics

ABSTRACT

A security platform architecture is described herein. The security platform architecture includes multiple layers and utilizes a combination of encryption and other security features to generate a secure environment.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation-in-part application of co-pending U.S. patent application Ser. No. 16/709,683, filed on Dec. 10, 2019, and titled “SECURITY PLATFORM ARCHITECTURE,” which is hereby incorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates to security. More specifically, the present invention relates to a security architecture.

BACKGROUND OF THE INVENTION

Although the Internet provides a massive opportunity for shared knowledge, it also enables those with malicious intentions to attack such as by stealing personal data or causing interference with properly functioning mechanisms. The Internet and other networks will continue to grow both in size and functionality, and with such growth, security will be paramount.

SUMMARY OF THE INVENTION

A security platform architecture is described herein. The security platform architecture includes multiple layers and utilizes a combination of encryption and other security features to generate a secure environment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of a security platform architecture according to some embodiments.

FIG. 2 illustrates an exemplary access-hardened API according to some embodiments.

FIG. 3 illustrates a diagram of a secure application architecture according to some embodiments.

FIG. 4 illustrates a diagram of a smart device and a CyberEye multi-factor authentication according to some embodiments.

FIG. 5 illustrates a flowchart of a method of implementing a security platform architecture according to some embodiments.

FIG. 6 illustrates a block diagram of an exemplary computing device configured to implement the security platform architecture according to some embodiments.

FIG. 7 illustrates a diagram of a secure application framework and platform according to some embodiments.

FIG. 8 illustrates a diagram of a secure key exchange through an opti-encryption channel according to some embodiments.

FIG. 9 illustrates a flowchart of a method of utilizing a user device as identification according to some embodiments.

FIG. 10 illustrates a diagram of an optical encryption implementation according to some embodiments.

FIG. 11 illustrates a diagram of an optical encryption implementation on multiple devices according to some embodiments.

FIG. 12 illustrates a diagram of an optical encryption implementation on multiple devices according to some embodiments.

FIG. 13 illustrates a diagram of multiple embedded electronic devices and/or other devices according to some embodiments.

FIG. 14 illustrates a diagram of a system for electronic transactions using personal computing devices and proxy services according to some embodiments.

FIG. 15 illustrates a flowchart of a method of device hand off identification proofing using behavioral analytics according to some embodiments.

FIG. 16 illustrates a flowchart of a method of an automated transparent login without saved credentials or passwords according to some embodiments.

FIG. 17 illustrates a diagram of a system configured for implementing a method of an automated transparent login without saved credentials or passwords according to some embodiments.

FIG. 18 illustrates a flowchart of a method of implementing automated identification proofing using a random multitude of real-time behavioral biometric samplings according to some embodiments.

FIG. 19 illustrates a flowchart of a method of implementing user identification proofing using a combination of user responses to system Turing tests using biometric methods according to some embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A security platform architecture is described herein. The security platform architecture includes multiple layers and utilizes a combination of encryption and other security features to generate a secure environment.

FIG. 1 illustrates a diagram of a security platform architecture according to some embodiments. The security platform 100 includes security-hardened code 102, secure network transport 104, security transport and data transformation modules 106, building block modules 108, application solutions/modules 110, access-hardened API/SDK 112, and a security orchestration server 114. In some embodiments, fewer or additional layers are implemented.

The security-hardened code 102 is able to include open or proprietary software security hardening. The security-hardened code 102 includes software libraries, executables, scripts, modules, drivers, and/or any other executable, accessible or callable data.

In some embodiments, the security-hardened code 102 is encrypted. For example, each library, executable and other data is encrypted. Furthering the example, an “encryption at rest” or “data at rest” encryption implementation is utilized. Data at rest encryption means data that is not in transit in a network or data that is not being executed is encrypted. Any data at rest encryption is able to be implemented including quantum encryption.

In some embodiments, the security-hardened code 102 is signed. For example, a digitally signed driver is associated with a digital certificate which enables identification of the publisher/owner of the driver.

In some embodiments, open or proprietary verification is based on encryption/decryption (e.g., the software modules/executables are inside an encrypted container), and is performed at installation and prior to each access. The security-hardened code 102 is fully tamper-proof. To be able to access the security-hardened code 102, a caller (e.g., calling module/procedure) should be part of the security domain.

In some embodiments, runtime verification of each executable, library, driver and/or data is implemented. Runtime verification is able to include any type of analysis of activity such as determining and learning keystrokes per user, or other mannerisms of computer interaction by each user.

In some embodiments, a security callback implementation is utilized. Before data is accessed or executed, the security callback calls to a master/server from the client, and if the hash or other verification implementation on the master/server does not match the hash/verification on the client, then access to the security-hardened code 102 is restricted/denied. For example, if a hash match fails, a software module will not be able to be executed, launched, moved or another action. The hash/verification comparison/analysis occurs before access of the security-hardened code 102. The security callback implementation is able to protect against instances where a virus or other malicious code has infiltrated a client device (e.g., mobile phone, personal computer).

The security-hardened code 102 is able to use any individual security technology or any combination of security technologies.

The security-hardened code 102 is able to be stored in a secure vault. The contents of the vault are encrypted using the data at rest encryption scheme. The contents of the vault are also signed. In some embodiments, white noise encryption is implemented which involves the use of white noise in the encryption. For example, white noise is generated using shift registers and randomizers, and the white noise is incorporated in the encryption such that if someone were to decrypt the content, they would obtain white noise.

The secure network transport 104 is able to be a high-speed, low-overhead, encrypted channel. In some embodiments, the secure network transport 104 uses quantum encryption (or post-quantum encryption). Quantum encryption is based on real keys (e.g., real numbers instead of integers) such that the encryption may not be hackable. Quantum encryption such as described in U.S. Provisional Patent Application No. 62/698,644, filed on Jul. 16, 2018, titled: “SECRET MATERIAL EXCHANGE AND AUTHENTICATION CRYPTOGRAPHY OPERATIONS,” and PCT Application No. PCT/US2019/041871, filed on Jul. 15, 2019, titled: “SECRET MATERIAL EXCHANGE AND AUTHENTICATION CRYPTOGRAPHY OPERATIONS,” which are both incorporated by reference herein in their entireties for all purposes, is able to be utilized herein.

In some embodiments, everything that communicates uses the secure network transport 104. For example, when a software module communicates with another software module, information is sent using the secure network transport 104.

The secure network transport 104 is able to utilize a proprietary or open Internet key exchange, Trusted Platform Module (TPM) key processing and storage, IoT key exchange, and/or optical/sonic/infrared/Bluetooth® key exchange.

The security transport and data transformation modules 106 implement “data in motion” encryption and “data at rest” encryption. In some embodiments, encryption is implemented while the data is being accessed/executed. The security transport and data transformation modules 110 include a tunneling module to tunnel the implementation inside Secure Sockets Layer (SSL)/Transport Layer Security (TLS) to enable the data to be utilized on any platform/browser/software/hardware/standard. The tunneling is able to be TLS quantum tunneling. The security transport and data transformation modules 106 include Application Programming Interfaces (APIs), keys, Public Key Infrastructure (PKI) modules, and/or other modules/structures.

The building block modules 108 include processes, services, microservices such as: AUTH, TRANS, LOG, ETRANS, BLUETOOTH, ULTRASONIC, and/or RF, which are implemented using objects (including functions or sub-routines). The building block modules 108 come from the software code/libraries and are able to communicate via the secure network transport 104.

The building block modules 108 are able to communicate between each other. In some embodiments, the module to module communications utilize Qrist encryption transport (or another encryption scheme) which isolates the modules from threats of hacks, viruses and other malicious entities. Qrist transport is high performance and low latency which requires almost no overhead. Since the building block modules 108 are pulled from the encrypted code/libraries, they are not typically visible in memory.

The building block modules 108 also have layered APIs (e.g., a specific API to communicate amongst each other). The APIs enable additional flexibility and extendability as well as providing a firewall (or micro-firewall) between every service to ensure transactions are coming from the right place (e.g., no man in the middle), the correct data is involved, and so on. The communications between the building block modules 108 are also able to be over HTTP. For example, a Web Application Firewall (WAF) is utilized, which applies specific rules for HTTP application communications.

The building block modules 108 are able to include executables (.exe), dynamic link libraries (.dll), configuration information, or other types of data/files (e.g., .so). The building block modules 108 are able to run in the background as background processes. The building block modules 108 are able to communicate through encrypted communications. The encrypted communications go through a transport such as Internet Protocol (IP), encrypted pipes in memory, Bluetooth® or another implementation. As described herein, the services are wrapped in APIs. The APIs implement REST (e.g., a very thin web server/client).

The application solutions/modules 110 are able to be developed using the building block modules 108. Exemplary applications include: encrypted email attachments, CyberEye multi-factor authentication, ID proofing, secure document signing (e.g., Docusign), secure electronic transactions, smart machines (e.g., autonomous vehicles), SAAS login, OpenVPN, blockchain login, blockchain support, high performance transaction services, electronic locks and E-notary. For example, since Docusign is relatively unsecure (e.g., anyone can sign the document), by combining Docusign with a CyberEye multi-factor authentication or another identification technology, it is possible to increase the security such that only the intended person is able to sign the document. More specifically, data at rest encryption is utilized to ensure the document is secure while stored, and the multi-factor authentication is used to ensure that the person signing the document is the desired target, and data in motion encryption is used to ensure the signed document is not tampered with and is received at the correct location.

The application solutions/modules 110 are able to be run/executed on any computing device such as a smart phone, a personal computer, a laptop, a tablet computer, a server, a dedicated smart device, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a portable music player, a mobile device, a video player, a video disc writer/player (e.g., DVD writer/player, high definition disc writer/player, ultra high definition disc writer/player), a television, a home entertainment system, an augmented reality device, a virtual reality device, smart jewelry (e.g., smart watch), a vehicle (e.g., a self-driving vehicle), IoT devices or any other suitable computing device.

The access-hardened API/SDK 112 includes similar security (e.g., encryption) as in the other modules. The access-hardened API/SDK 112 is able to utilize REST or another API (e.g., RPC). By implementing the access-hardened API/SDK 112, communication with the outside world is facilitated. For example, using a scripting language (e.g., javascript), an external application is able to communicate with the system.

The security orchestration server 114 is/includes a scripting language where when a call is received, the process goes down through the stacks starting at the top until the software library/code is reached (e.g., 114 through 102), and then the process goes up through the stacks out through the top (e.g., 102 through 114). Although the language is exposed to the outside world, it is based on the hardened code 102, so it is still secure.

The security orchestration server 114 accesses the security-hardened code 102 in the secure vault. The security orchestration server 114 includes keys and other information used for accessing the security-hardened code 102. The security orchestration server 114 deploys the services, builds keys, assigns commands/tasks and performs other control features. In some embodiments, the security orchestration server 114 organizes the building block modules 108 such that they are able to communicate with each other and function as an application 110.

When the security orchestration server 114 launches an application 110 (comprised of the building block modules 108), the security orchestration server 114 retrieves .dlls or other data and executes/communicates with the application 110 through the APIs of the building block modules 108.

The security orchestration server 114 controls deployment, policies and app structure. The app structure is also referred to as the application solutions/modules 110 which includes the code, the different modules/objects, and any data involved. The policies are able to be any policies such as for the firewall—what ports are open, which APIs are able to run in/with the application, who/what/when/where, well-structure calls (size of packets, and more), ports/ACL, and partners (which partners have access).

The secure orchestration server 114 implements a secure language such as python with extensions, java, and/or javascript.

In an example, a copy program is implemented by sending a copy command via the API which triggers a copy module which uses the transport scheme including data at rest encryption and data in motion encryption, and then goes to the transport layer and performs encryption/decryption, handles key exchanges and the copying using the code modules for copying.

FIG. 2 illustrates an exemplary access-hardened API according to some embodiments. The building block modules 108 enable communications and actions which are handled via RESTful APIs. Additionally, APIs 200 include Web Application Firewall (WAF) features to ensure that any communication between the building block modules 108 is secure/protected.

FIG. 3 illustrates a diagram of a secure application architecture according to some embodiments. An exemplary CyberEye implementation is able to be used to perform opti-crypto wireless airgap access (somewhat similar to a QR code). The building block modules 108 hardened by APIs 200 form the hardened APIs 112 which enable a modular services design, where each module is generalized for use in multiple application solutions. As described, the modules communicate with each other using encrypted communications (e.g., HTTP secure protocol). An API/WAF firewall is embedded in each module.

FIG. 4 illustrates a diagram of a smart device and a CyberEye multi-factor authentication according to some embodiments. As described in U.S. patent application Ser. No. 15/147,786, filed on May 5, 2016, titled: “Palette-based Optical Recognition Code Generators and Decoders” and U.S. patent application Ser. No. 15/721,899, filed on Sep. 30, 2017, titled: “AUTHENTICATION AND PERSONAL DATA SHARING FOR PARTNER SERVICES USING OUT-OF-BAND OPTICAL MARK RECOGNITION,” which are incorporated by reference herein in their entireties for all purposes, a smart device 400 (e.g., smart phone) is able to utilize an application (and camera) on the smart device 400 to scan a CyberEye optical recognition code mark displayed on another device 402 (e.g., personal computer or second smart device) to perform multi-factor authentication. As described herein, the CyberEye multi-factor authentication is an application module which is composed of building block modules which transport data securely using a secure network transport, where the building block modules are composed of software code which is securely stored and accessed on the smart device 400. The CyberEye multi-factor authentication is an example of an application executable using the security platform architecture.

FIG. 5 illustrates a flowchart of a method of implementing a security platform architecture according to some embodiments. In the step 500, an application is accessed as part of a web service such that a security orchestration server or access-hardened API is used to access the application. In the step 502, the application is executed. The application is composed of building block modules which transport data securely using a secure network transport, in the step 504. The building block modules are composed of software code which is securely stored and accessed on a device, in the step 506. Secure access involves data at rest encryption/decryption as well as data in motion encryption/decryption. In some embodiments, encryption/decryption involves quantum encryption/decryption using real numbers. In some embodiments, transporting the data includes utilizing tunneling such that the data is secure but also able to be transmitted over standard protocols. In some embodiments, fewer or additional steps are implemented. For example, in some embodiments, the application is a standalone application not accessed as part of a web service. In some embodiments, the order of the steps is modified.

FIG. 6 illustrates a block diagram of an exemplary computing device configured to implement the security platform architecture according to some embodiments. The computing device 600 is able to be used to acquire, store, compute, process, communicate and/or display information. The computing device 600 is able to implement any of the security platform architecture aspects. In general, a hardware structure suitable for implementing the computing device 600 includes a network interface 602, a memory 604, a processor 606, I/O device(s) 608, a bus 610 and a storage device 612. The choice of processor is not critical as long as a suitable processor with sufficient speed is chosen. The memory 604 is able to be any conventional computer memory known in the art. The storage device 612 is able to include a hard drive, CDROM, CDRW, DVD, DVDRW, High Definition disc/drive, ultra-HD drive, flash memory card or any other storage device. The computing device 600 is able to include one or more network interfaces 602. An example of a network interface includes a network card connected to an Ethernet or other type of LAN. The I/O device(s) 608 are able to include one or more of the following: keyboard, mouse, monitor, screen, printer, modem, touchscreen, button interface and other devices. Security platform architecture application(s) 630 used to implement the security platform architecture are likely to be stored in the storage device 612 and memory 604 and processed as applications are typically processed. More or fewer components shown in FIG. 6 are able to be included in the computing device 600. In some embodiments, security platform architecture hardware 620 is included. Although the computing device 600 in FIG. 6 includes applications 630 and hardware 620 for the security platform architecture, the security platform architecture is able to be implemented on a computing device in hardware, firmware, software or any combination thereof. For example, in some embodiments, the security platform architecture applications 630 are programmed in a memory and executed using a processor. In another example, in some embodiments, the security platform architecture hardware 620 is programmed hardware logic including gates specifically designed to implement the security platform architecture.

In some embodiments, the security platform architecture application(s) 630 include several applications and/or modules. In some embodiments, modules include one or more sub-modules as well. In some embodiments, fewer or additional modules are able to be included.

In some embodiments, the security platform architecture hardware 620 includes camera components such as a lens, an image sensor, and/or any other camera components.

Examples of suitable computing devices include a personal computer, a laptop computer, a computer workstation, a server, a mainframe computer, a handheld computer, a personal digital assistant, a cellular/mobile telephone, a smart appliance, a gaming console, a digital camera, a digital camcorder, a camera phone, a smart phone, a portable music player, a tablet computer, a mobile device, a video player, a video disc writer/player (e.g., DVD writer/player, high definition disc writer/player, ultra high definition disc writer/player), a television, a home entertainment system, an augmented reality device, a virtual reality device, smart jewelry (e.g., smart watch), a vehicle (e.g., a self-driving vehicle), IoT devices or any other suitable computing device.

FIG. 7 illustrates a diagram of a secure application framework and platform according to some embodiments. The secure application framework and platform includes: a secure vault 700, a secure orchestration server 114 (also referred to as an orchestrator), and a set of building block modules 108 which form an application implemented via an access-hardened API 112. As described herein, the secure vault 700 stores the code 102 using encryption (e.g., white noise encryption) and signing, where the code 102 is used to generate/form the building block modules 108 which when organized form an application. The secure orchestration server 114 is able to control access to the code, deploy services, control one or more policies, and organize the one or more building block modules. Additional or fewer components are able to be included in the secure application framework and platform.

FIG. 8 illustrates a diagram of a secure key exchange through an opti-encryption channel according to some embodiments. Device A sends a first key to Device B, and Device B sends the first key and a second key back to Device A. Then Device A sends a final key to Device B, where the final key is based on the first key and the second key. In some embodiments, the final key is computed using the first key and the second key and one or more equations (e.g., linear equations). In some embodiments, white noise is inserted into the final key, or the final key is wrapped in white noise. In some embodiments, the keys are real numbers instead of integers.

In some embodiments, the final key is protected by optical encryption. As described herein, a user uses a camera device such as a camera on a mobile phone or tablet to scan/acquire a dynamic optical mark (e.g., CyberEye mark). The CyberEye result is wrapped around the final key. In some embodiments, the final key (with white noise) is encrypted/wrapped using the CyberEye encryption (or other opti-crypto wireless airgap encryption) information. In some embodiments, the opti-crypto key wrapper is a key encapsulation algorithm. In some embodiments, the optical encryption is used to generate the key. For example, the CyberEye result is a key or the final key which is combined with white noise.

Once the keys are passed, an encrypted communication/channel is able to be established (e.g., AES). In some embodiments, the encryption used is polymorphic, meaning the keys for the packets continuously change. In some embodiments, the encryption utilized with the encrypted communication/channel is post quantum encryption which enables quantum resistant encryption.

In some embodiments, a user's computing device is able to be used as a secure identification (e.g., ID proofing). The computing device is able to have a TPM or similar device/implementation for securing certificates. The TPM or similar implementation has break-in detection and other security measures. The computing device also includes machine learning implementations (processors/microchips). The computing device is able to include other standard components such as a CPU, one or more cameras, a screen, communication modules (e.g., Bluetooth,® WiFi, 5G, xG), and others.

ID proofing is able to prove/guarantee a user is who they claim to be. Instead of or in addition to biometric identification (e.g., fingerprint matching) and facial/voice recognition, other aspects of a user or a user's actions are able to be analyzed (e.g., behavior analysis). For example, a user's gate/stride, how the user uses his device, how the user types/swipes, and other motions/actions/transactions are able to be analyzed, compared and matched to determine if the user is the expected/appropriate user. Furthering the example, if a user typically takes short strides while using the phone and uses two thumbs to input text, then when a second user attempts to use the phone but has longer strides and uses a single finger input, then the device is able to detect that the person using the device is not the expected user (e.g., owner of the mobile phone).

A trust score is able to be generated based on the analysis. For example, as more matches are made (e.g., valid biometric input, matching stride, and matching typing performance, the trust score increases). Policies are able to implemented based on the trust score. For example, one or more thresholds are able to be utilized such that if the trust score is below a threshold, then options are limited for that user. Furthering the example, if a user has a 100% trust score, then there are no limitations on the user's use of the device, but if the user has a 50% trust score, below a money threshold, then the user is not able to perform any transactions involving money with the device, and if the user has a 5% trust score, the user is not able to access any applications of the device. Any number of thresholds are able to be used, and any limitations/consequences are able to be implemented based on the thresholds/trust score. The orchestrator described herein is able to implement these policies. In some embodiments, a risk score is implemented which is similar but inverse of the trust score.

In some embodiments, a transaction proxy is implemented. The transaction proxy is able to utilize the trust score to determine which transactions are allowed. The transactions are able to include any transactions such as logging in to a web site/social media, accessing an application (local/online), purchasing goods/services, transferring money, opening a door, starting a car, signing a document or any other transaction. In some embodiments, if a user's trust score is currently below a threshold, the device is able to perform additional tests of the user to increase their trust score (e.g., ask the user to say a word to determine a voice match). Passwords and personal information are able to be stored locally on the device (or on the Internet/cloud) for retrieval for access/comparison purposes. As described herein, the data (e.g., passwords and personal information) are able to be encrypted and backed up. For example, if the device is lost, the backup enables a user to purchase another device and retrieve all of the passwords/personal information.

In some embodiments, the implementation is or includes an extensible transaction method. For example, the device includes an application with a list of transactions (e.g., plug-ins). Once a transaction is initiated (e.g., Facebook login where Facebook password is pulled from the TPM), the transaction with all of the required information is stored as an encrypted file which is sent to a secure server proxy which is able to decrypt the file and then make the transaction. Since the transaction is able to occur using a proxy, the user is able to remain anonymous. In some embodiments, the opti-encryption implementation is able to be utilized with the secure identification implementation.

FIG. 9 illustrates a flowchart of a method of utilizing a user device as identification according to some embodiments. In the step 900, user information is acquired. The user information is able to be acquired in any manner such as receiving and logging keystrokes/touches from a keyboard/digital keypad/touch screen, measuring movement using an accelerometer or other device in a mobile device, acquiring imaging information using a camera (e.g., camera phone), acquiring voice information using a microphone, and/or any other implementation described herein.

In the step 902, a trust score is generated. The trust score is generated by analyzing the acquired user information. For example, an application records (and learns) how a user types, and compares how the current input with previous input to determine similarities. Similarly, the application is able to analyze a user's stride (long, short, fast, slow) by capturing the data over periods of time for comparison purposes. The trust score is also able to be based on other information such as location, time, device information and other personal information. For example, if the device is determined to be in Mexico, and the user has never visited Mexico previously, the trust score is able to be decreased. Or if the device is being used at 3a, when the user does not use the device after 10p or before 6a, then the trust score is decreased.

In the step 904, usability of the device is limited based on the trust score. For example, if the trust score is below a minimum threshold, the user may be prevented from doing anything on the device. In another example, if the user's trust score is determined to be below an upper threshold, the user may be permitted to utilize apps such as gaming apps, but is not able to use the device to make purchases, sign documents or login to social media accounts. In some embodiments, actions/transactions are classified into classes or levels, and the classes/levels correspond to ranges of trust scores or being above or below specified thresholds. For example, purchases of $10 or more and signing documents are in Class 1, and Class 1 actions are only available when a trust score is 99% or above, and purchases below $10 and social media logins are in Class 2, and Class 2 actions are available when a trust score is 80% or above.

In some embodiments, fewer or additional steps are implemented. For example, if a user's trust score is below a threshold for an action that the user wants to take, the device is able to request additional proof by the user (e.g., provide a fingerprint and/or input a secret code) to increase the user's trust score. In some embodiments, the order of the steps is modified.

FIG. 10 illustrates a diagram of an optical encryption implementation according to some embodiments. As described herein, a device 1000 (e.g., smart phone) includes a camera which is able to acquire an image of a CyberEye implementation (e.g., repeating pattern) displayed in a web browser on another device 1002 (e.g., personal computer). The web browser is able to come from a server 1004 (e.g., local server). The server is able to provide authentication. There is also a back channel from the server to the device 1000. As described herein, the device 1000 is able to be used as a user's ID.

FIG. 11 illustrates a diagram of an optical encryption implementation on multiple devices according to some embodiments. The CyberEye implementation (or other optical multi-factor authentication) is able to be implemented on a gas station pump, Automated Teller Machine (ATM) machine, or any other device capable of displaying a multi-factor authentication implementation. For example, the gas station pump or ATM includes a display which is capable of displaying a web browser with a CyberEye implementation. The user is then able to use his mobile device to scan/acquire an image of the CyberEye, and then based on the ID proofing described herein, the user's device is able to authenticate payment or perform other transactions with the gas station pump, ATM or other device.

FIG. 12 illustrates a diagram of an optical encryption implementation on multiple devices according to some embodiments. In some embodiments, instead of or in addition to implementing a display with a CyberEye (or similar) implementation an embedded electronic device 1200 is utilized. The embedded electronic device 1200 includes a camera 1202 and lights 1204 (e.g., LEDs). In addition, other standard or specialized computing components are able to be included such as a processor, memory and a communication device (e.g., to communicate with WiFi).

In some embodiments, the embedded electronic device 1200 illuminates/flashes the lights 1204 in a specific pattern which a user device 1210 (e.g., smart phone) is able to scan/capture (similar to the CyberEye implementation). For example, upon the user device 1210 scanning the pattern provided by the embedded electronic device 1200, the user device 1210 (or the embedded electronic device 1200) sends an encrypted communication to perform a transaction. In some embodiments, a server 1220 determines (based on stored policies as described herein) whether the user's trust score is above a threshold to perform the transaction. For example, the user device 1210 is able to be used to unlock a house door, open a car door or purchase items at a vending machine. Furthering the example, in an encrypted communication to the server 1220 based on the scan of the embedded electronic device 1200, a transaction request to open the front door is sent to the server 1220 (either by the embedded electronic device 1200 or the user device 1210). The server 1220 compares the trust score with policies (e.g., if trust score is 99% or above, then unlock the lock; otherwise, no operation), and performs or rejects the requested transaction. For example, the server 1220 sends a communication to the embedded electronic device 1200 to unlock the lock of the door. The communication is able to be sent to a local or remote server for authentication which then communicates to the specific device (e.g., house door lock), or the communication is sent directly to the specific device (e.g., peer-to-peer communication). In some embodiments, the embedded electronic device 1200 sends the communication to a local or remote server for authentication, and then upon receiving authentication, the embedded electronic device 1200 performs the transaction. In some embodiments, the embedded electronic device 1200 communicates with the server (e.g., communicates the transaction request), and the user device 1210 communicates with the server (e.g., the user ID/trust score), and the server uses the information received from both devices to perform an action or to send a communication to perform an action, as described herein.

FIG. 13 illustrates a diagram of multiple embedded electronic devices and/or other devices according to some embodiments. In some embodiments, an embedded electronic device 1200 is able to communicate with one or more embedded electronic devices 1200. In some embodiments, an embedded electronic device 1200 is able to communicate with one or more other devices (e.g., user device 1210). In some embodiments, a user device 1210 is able to communicate with one or more other devices (e.g., user device 1210).

Since the embedded electronic device 1200 includes a camera 1202 and LEDs 1204, and a user device 1210 (e.g., mobile phone) includes a camera and a display to display a CyberEye (or similar) implementation, each is able to be used to display and acquire a unique code.

The multiple devices are able to communicate with each other and/or with a server. For example, a first user device is able to communicate with a second user device, and the second user device communicates with a server, and then provides the data received from the server to the first user device. Therefore, in some embodiments, the first user device (or embedded electronic device) does not need a connection with the server.

In some embodiments, the user device is able to replace a car key fob, since the user device is able to perform ID proofing as described herein, and is able to communicate with an embedded electronic device (e.g., a vehicle door lock/other vehicle controls). Similarly, with minimal modification, a car key fob is able to implement the technology described herein.

In some embodiments, instead of using optics for encryption (e.g., scanning a CyberEye implementation), other schemes are used such as infra-red, Bluetooth®, RFID, sonic, ultrasonics, laser, or RF/WiFi.

FIG. 14 illustrates a diagram of a system for electronic transactions using personal computing devices and proxy services according to some embodiments. A user device 1400 (e.g., smart phone) scans a CyberEye or similar implementation on a second device 1402 (e.g., personal computer or mobile device). The user device 1400 and/or the second device 1402 are able to communicate with a server 1404.

In some embodiments, the user device 1400 includes a transaction application 1410 programmed in memory. The transaction application 1410 is configured to send an encrypted package 1412 to the server 1404 based on the scan of the CyberEye or similar implementation (e.g., dynamic optical mark/code). The transaction application 1410 is able to trigger actions such as log in to a social media site, log in to a bank account, perform a monetary transfer, and/or any other transaction.

The server 1404 implements a proxy to perform the electronic transactions such as authentication, unlock door, moving money, e-signature and/or any other transaction. The transactions available through the transaction application 1410 are also added to the server 1404, such that the number of transactions is extensible. As described herein, the transactions are able to be accompanied by a trust or risk score such that if the trust/risk score is above or below a threshold (depending on how implemented), then the transaction request may be denied. By using the proxy to perform the electronic transactions, a user's anonymity and security is able to be maintained. With a transaction directly from a user device 1400, there is still potential for eavesdropping. However, as mentioned above, the transaction application 1410 sends an encrypted package/packet (e.g., token), which includes the transaction information (e.g., transaction ID, phone ID, trust score, specific transaction details such as how much money to transfer) to the server, where the proxy performs the transaction. The proxy server has secure connections to banks, Paypal, social networking sites, and other cloud servers/services. Furthermore, in some embodiments, the proxy server communication does not specify details about the user. In some embodiments, after the proxy server performs the transaction, information is sent to the user device. In some embodiments, the information sent to the user device is encrypted. For example, after the proxy server logs in to Facebook, the Facebook user page is opened on the user device.

In an example, a user receives a document to sign on the second device 1402. The user clicks the document icon to open the document, which then causes a CyberEye mark to appear. The user then scans the CyberEye mark with the user device 1400 which performs the ID proofing/authentication as described herein. The document is then opened, and it is known that the person who opened the document is the correct person. Similarly, the document is able to be signed using the CyberEye mark or a similar implementation to ensure the person signing the document is the correct person.

As described herein, a user device (e.g., mobile phone) is able to be used for ID proofing, where the user device recognizes a user based on various actions/input/behavioral/usage patterns (e.g., voice/facial recognition, stride/gate, location, typing technique, and so on). In some embodiments, potential user changes are detected. For example, if a user logs in, but then puts the device down, another user may pick up the phone, and is not the original user. Therefore, actions/situations such as putting the phone down, handing the phone to someone else, leaving the phone somewhere are able to be detected. Detecting the actions/situations is able to be implemented in any manner such as using an accelerometer to determine that the phone is no longer moving which would indicate that it was put down. Similarly, sensors on the phone are able to determine that multiple hands are holding the phone which would indicate that the phone is being handed to someone else. In some embodiments, the user device is configured to determine if a user is under duress, and if the user is under duress, the trust score is able to be affected. For example, an accelerometer of the user device is able to be used to determine shaking/trembling, and a microphone of the device (in conjunction with a voice analysis application) is able to determine if the user's voice is different (e.g., shaky/trembling). In another example, the camera of the user device is able to detect additional people near the user and/or user device, and if the people are unrecognized or recognized as criminals (e.g., face analysis with cross-comparison of a criminal database), then the trust score drops significantly (e.g., to zero).

As discussed herein, when a user attempts to perform an action/transaction where the user's trust score is below a threshold, the user is able to be challenged which will raise the user's trust score. The challenge is able to be a behavioral challenge such as walking 10 feet so the user device is able to analyze the user's gate; typing a sentence to analyze the user's typing technique; or talking for 10 seconds or repeating a specific phrase. In some embodiments, the user device includes proximity detection, fingerprint analysis, and/or any other analysis.

In some embodiments, an intuition engine is developed and implemented. The intuition engine continuously monitors a user's behavior and analyzes aspects of the user as described herein. The intuition engine uses the learning to be able to identify the user and generate a trust score.

With 5G and future generation cellular networks, user devices and other devices are able to be connected and accessible at all times, to acquire and receive significant amounts of information. For example, user device locations, actions, purchases, autonomous vehicle movements, health information, and any other information are able to be tracked, analyzed and used for machine learning to generate a behavioral fingerprint/pattern for a user.

In some embodiments, when a user utilizes multiple user devices, the user devices are linked together such that the data collected is all organized for the user. For example, if a has a smart phone, a smart watch (including health monitor), and an autonomous vehicle, the data collected from each is able to be stored under the user's name, so that the user's heart beat and driving routes and stride are able to be used to develop a trust score for when the user uses any of these devices.

To utilize the security platform architecture, a device executes an application which is composed of building block modules which transport data securely using a secure network transport, where the building block modules are composed of software code which is securely stored and accessed on the device. In some embodiments, the application is accessed as part of a web service such that a security orchestration server or access-hardened API are used to access the application. The security platform architecture is able to be implemented with user assistance or automatically without user involvement.

In operation, the security platform architecture provides an extremely secure system capable of providing virtually tamper-proof applications.

The security platform architecture implements/enables: a unique Opti-crypto wireless airgap transport, a personal smart device—intelligent ID proofing, secure extensible electronic transaction framework, blockchain integration and functionality, anonymous authentication and transaction technology, post quantum encryption at rest and in motion, secure private key exchange technology, secure encryption tunneled in TLS, high-throughput, low-latency transport performance, low overhead transport for low power FOG computing applications such as IOT, RFID, and others.

The security platform architecture is able to be utilized with:

Consumer applications such as games, communications, personal applications;

Public Cloud Infrastructure such as SAAS front-end security, VM-VM, container-container security intercommunications;

Private Cloud/Data Centers such as enhanced firewall, router, edge security systems; Telco Infrastructures such as CPE security, SDN encrypted tunnels, MEC edge security and transports, secure encrypted network slicing; and

5G New Market Smart Technologies such as smart machine security (sobots, autonomous vehicles, medical equipment).

The security platform includes infrastructure building blocks:

Client devices:

smart personal devices, IoT devices, RFID sensors, embedded hardware, smart machines;

Client functions:

ID proofing (trust analysis), CyberEye wireless transport, extensible electronic transaction clients, content and data loss security management, authorization client;

Transport functions:

Post-quantum data encryption technology, data-in-motion transport, data-at rest encryption, quantum tunnel through SSL/TLS, private-private secure key exchange, high-performance, low latency, low compute transport, TPM key management, SSL inspection;

Central server functions:

AAA services, federation gateway, electronic transactions server, adaptive authentication services, ID proofing services, user registration services, CyberEye transport server.

The security platform architecture is able to be used in business: 5G encrypted network slicing, electronic stock trading, vending machine purchasing interface, vehicle lock and security interfaces, anonymous access applications, Fog computing security transport (IoT to IoT device communications), SSL inspection security (decryption zones), generic web site/web services login services, MEC (mobile/multi-access edge gateway transport and security), cloud network backbone security firewalls (rack to rack FW), Office 365 secure login, low power IoT sensors, password management with single sign-on, high-security infrastructures requiring out-of-band or air gap enhanced access, or VM-to-VM (or containers) secure communications transport.

In some embodiments, device hand off identification proofing using behavioral analytics is implemented. For example, a device (e.g., mobile phone) detects when the device leaves a user's possession (e.g., put down on table, handed to another person). Based on the detection, when the device is accessed again, determination/confirmation that the user is the correct user is performed. In some embodiments, even if the device has not been placed in a locked mode (e.g., by a timeout or by the user), the device automatically enters a locked mode upon detecting leaving the user's possession.

FIG. 15 illustrates a flowchart of a method of device hand off identification proofing using behavioral analytics according to some embodiments. In the step 1500, a device detects that the device has left a user's possession. The device is able to be any device described herein (e.g., a mobile phone). Detecting that the device is no longer in the user's possession is able to be performed in any manner such as detecting that the device has been set down or handed off to another user. Other causes of a change in the user's possession are able to be detected as well such as a dropped device. In some embodiments, continuous monitoring of the device's sensors is implemented for detection, and in some embodiments, the sensors provide information only when triggered, or a combination thereof.

Detecting the device has been set down is able to be performed using a sensor to detect that the device is stationary, using a proximity sensor, or any other mechanism. For example, one or more accelerometers in the device are able to detect that the device is in a horizontal position and is not moving (e.g., for a period of time above a threshold), so it is determined to have been set down. Determining the device has been set down is able to be learned using artificial intelligence and neural network training. For example, if a user typically props up his device when he sets it down, the general angle at which the device sits is able to be calculated/determined and recorded and then used for comparison purposes. In another example, the device includes one or more proximity sensors which determine the proximity of the device to another object. For example, if the proximity sensors detect that the object is immediately proximate to a flat surface, then the device has been determined to have been set down. In some embodiments, multiple sets of sensors work together to determine that the device has been set down. For example, the accelerometers are used to determine that the device is lying horizontally, the proximity sensors are used to determine that the device is proximate to an object, and one or more motion sensors detect that the device has not moved for 3 seconds. The cameras and/or screen of the device are able to be used as proximity sensors to determine an orientation and/or proximity of the device to other objects. The microphone of the device is able to be used as well (e.g., to determine the distance of the user's voice and the changes of the distances, in addition to possibly the distance and/or changes of distance of another person's voice). For example, if the user's voice is determined to be from a distance above a threshold (e.g., based on acoustic analysis), then it is able to be determined that the user has set the device down.

The process of setting a device down is able to be broken up and analyzed separately. For example, some users may place a device down in a certain way, while other users may make certain motions before putting the device down. Furthering the example, the steps of setting the phone down are able to include: retrieving the device, holding the device, moving the device toward an object, placing the device on the object, and others. Each of these steps are able to be performed differently, so breaking down the process of setting down the device in many steps may be helpful in performing the analysis/learning/recognition of the process. In some embodiments, the steps are, or the process as a whole is, able to be classified for computer learning. For example, one class of setting the phone down is labeled “toss,” where users throw/toss their device down which is different from “gentle” where users gently/slowly place their device down. The “toss” versus “gentle” classifications are able to be determined as described herein such as based on the accelerometer and/or gyroscope information. In another example, some users hold the device vertically before placing it down, while others hold it horizontally, or with one hand versus two hands. The classifications are able to be used for analysis/comparison/matching purposes. Any data is able to be used to determine the device being set down (e.g., movement, proximity, sound, scanning/video, shaking, touch, pressure, orientation and others) using any of the device components such as the camera, screen, microphone, accelerometers, gyroscopes, sensors and others.

Detecting the device has been handed off is able to be performed in any manner. For example, sensors on/in the device are able to detect multiple points of contact (e.g., 4 points of contact indicating two points from one user's hand and two points from a second user's hand, or a number of points above a threshold). In another example, the accelerometers and/or other sensors (e.g., proximity sensors) are able to analyze and recognize a handoff motion (e.g., the device moving from a first position and moving/swinging outward to a second position, or side-to-side proximity detection). In some embodiments, a jarring motion is also able to be detected (e.g., the grab by one person of the device from another person). The handoff motion/pattern is able to be learned using artificial intelligence and neural network training. In some embodiments, motions/movements from many different users are collected and analyzed to determine what movements are included in a handoff. Furthermore, each user's movements are able to be analyzed separately to determine a specific handoff for that user. For example, User A may hand off a device to another user in an upright position after moving the device from his pocket to an outreached position, while User B hands off a device in a horizontal position after moving the device in an upward motion from the user's belt.

Each separate aspect of the movement is able to be recorded and analyzed as described herein to compile motion information for further pattern matching and analysis. For example, the hand off motion is able to be broken down into separate steps such as retrieval of the device by a first person, holding of the device, movement of the device, release of the device, and acquisition of the device by the second person. Each of the separate steps are able to be recorded and/or analyzed separately. Each of the separate steps are, or the process as a whole is, able to be classified/grouped which may be utilized with computer learning and/or matching. Any data is able to be used to determine a handoff (e.g., movement, proximity, sound, scanning/video, shaking, touch, pressure, orientation and others) using any of the device components such as the camera, screen, microphone, accelerometers, gyroscopes, sensors and others.

Similarly, other changes of a user's possession are able to be detected such as the device being dropped. For example, the accelerometers are able to detect rapid movement followed by a sudden stop or slight reversal of movement. Similar to the hand off and set down, dropping and other changes of possession are able to be analyzed and learned.

In the step 1502, a trust score drops/lowers (e.g., to 0) after detection of a loss of possession. As described herein, the trust score of the user determines how confident the device is that the person using the device is the owner of the device (e.g., is the user actually User A). In some embodiments, factors are analyzed to determine the amount the trust score drops. For example, if the device is set down for a limited amount of time (e.g., less than 1 second), then the trust score is halved (or another amount of reduction). If the device is set down for a longer amount of time (e.g., above a threshold), then the trust score drops by a larger amount (or to 0). In another example, if the device is handed off, the trust score drops (e.g., to 0). In some embodiments, in addition to the trust score dropping, the device enters a locked/sleep mode.

In some embodiments, a device has different trust scores for multiple users. For example, if a family uses the same mobile phone—Mom, Dad, Son and Daughter each have different recognizable behaviors (e.g., motion/typing style) to determine who is currently using the phone. Each user has an associated trust score as well. For example, a device may have a trust score of 0 after being set down, but then after the device is picked up, it is determined that Mom is using the device, so her trust score is elevated (e.g., 100), but after a handoff, the trust score goes to 0, until it is determined that Dad is using the device, and his trust score is elevated (e.g., 100). In some embodiments, certain users have certain capabilities/access/rights on a device. For example, if the device detects Mom or Dad, then purchases are allowed using the device, but if Son or Daughter are detected, the purchasing feature is disabled.

In the step 1504, a challenge is implemented to verify/re-authorize the user. The challenge is able to include biometrics, a password request, a question challenge, favorite image selection, facial recognition, 3D facial recognition and/or voice recognition. In some embodiments, the device performs behavioral analytics as described herein to determine if the user is the owner/designated user of the device. For example, analysis is performed on the user's movements of the device, touch/typing techniques, gait, and any other behaviors. Based on the behavioral analytics, the trust score may rise. For example, if the behavioral analytics match the user's behaviors, then the trust score will go up, but if they do not match, it is determined that the device is being used by someone other than the user, and the trust score stays low or goes down. In some embodiments, the challenge enables initial access to the device, but the user's trust score starts low initially (e.g., 50 out of 100), and then based on behavioral analytics, the trust score rises.

In some embodiments, fewer or additional steps are implemented. In some embodiments, the order of the steps is modified.

In some embodiments, an automated transparent login without saved credentials or passwords is implemented. In the past, a device's browser could save a user's login and password information. However, this is a very vulnerable implementation, and once a hacker or other malicious person acquires the user's login and password information, the hacker is able to perform tasks with the user's account just as the user could, and potentially steal from an online bank account or make purchases on an online shopping site. Using a trust score and behavioral analytics, logging in to websites and other portals is able to be implemented automatically.

FIG. 16 illustrates a flowchart of a method of an automated transparent login without saved credentials or passwords according to some embodiments. In the step 1600, a trust score is determined using behavioral analytics as described herein. For example, based on user movement, typing style, gait, device possession, and so on, a trust score is able to be determined. Furthering the example, the closer each analyzed aspect of the user (e.g., gait) is to the stored user information, the higher the trust score. In another example, if the user typically types on his device using his thumbs, and the current person using the device is using his index finger, then the trust score is adjusted (e.g., lowered). In contrast, if the user has a distinct gait (e.g., typically walks with the device in his hand, while he swings his arms moderately), and the device detects that the current person walking with the device in his hand while swinging his arms moderately, the trust score increases.

In some embodiments, in addition to a trust score, a confidence score is determined for the user/device. In some embodiments, the confidence score for a user is based on the trust score and a risk score. In some embodiments, the risk score is based on environmental factors, and the trust score is based on behavioral factors. In some embodiments, the confidence score goes up when the trust score goes up, and the confidence score goes down when the risk score goes up. Any equation for the confidence score is possible, but in general as the trust increases, the confidence increases, but as the risk increases the confidence decreases.

In the step 1602, a multi-factor authentication (MFA) application is executed. The MFA application is able to be running in the foreground or the background. The MFA application is able to be implemented in a secure, isolated space as described herein to prevent it from being compromised/hacked. In some embodiments, the MFA application includes aspects (e.g., operations) to acquire information to determine the trust, risk and confidence scores. For example, the trust score and risk scores each have multiple factors which go into determining their respective scores which are used to determine the confidence score which is further used for authenticating a user.

In some embodiments, the MFA application utilizes the confidence score analysis and additional user verification implementations. For example, CyberEye (also referred to as CypherEye) application/technology is able to be executed with the device. In some embodiments, the MFA application and/or CypherEye application is used as a login authority. The MFA login or CypherEye login looks like a local login, but instead a hash (or other information) is sent to a backend mechanism. In some embodiments, the MFA application uses the CypherEye information in conjunction with the confidence score. In some embodiments, a challenge is implemented (e.g., a request for the user to perform a CypherEye operation) for additional verification/qualification. For example, if a user's confidence score is below a threshold, then the user is challenged with a CypherEye request to acquire a CypherEye mark with his device. In another example, a user is able to log in using the MFA application which gives the user access to basic phone functions (e.g., using Facebook), but to access banking/trading applications or web sites, the user is presented a challenge (e.g., security question, password, CypherEye acquisition using camera) for further verification.

In some embodiments, the challenge is only presented if the confidence score is not above a threshold. For example, if the user has a confidence score of 99 out of 100 on the device, then the user is not requested to perform additional authentication measures to gain access to web sites or applications. However, if the user has a confidence score of 50 out of 100, then additional authentication measures are utilized before access is given to certain web sites or applications. For example, although the user logged in using the MFA application, the device or system determined that the same user logged in (or attempted to) using a different device 500 miles away. The risk score is elevated since one of the log in attempts was likely not from a valid user, so the confidence score was lowered. A challenge may be presented in this situation.

In some embodiments, the MFA application is used in conjunction with a login/password. For example, a browser presents a web page for a user to input login information and a corresponding password as well as MFA information (e.g., a scanned CypherEye code/mark).

In some embodiments, the MFA application is a plugin for the browser.

In the step 1604, the MFA application (or plugin) contacts a server and/or backend device (e.g., Visa or PayPal) based on the MFA information (e.g., behavioral information or other acquired information). For example, the MFA application sends the confidence score as determined. In another example, the MFA application sends the acquired information to the server for the server to determine the confidence score. In some embodiments, the confidence score is utilized by the server such that if the confidence score is above a threshold, the server contacts the backend device with the user login information. Furthering the example, the server stores user login/password information to the backend device, and once the user is verified by the server based on the MFA information, then the server communicates the login/password information with the backend device to gain access for the user device. The MFA application and/or the server are able to implement a proxy authentication or other implementation to gain access to the backend device. In some embodiments, the MFA application acts as a proxy server, if the confidence score of the user is above a threshold (e.g., 90 out of 100).

In the step 1606, login authorization is provided by a backend device (e.g., allow the user to access a web page populated with the user's specific information (e.g., bank account information)). For example, the server (or proxy server) provides a login request with the appropriate credentials, and the backend device accepts the request and allows access to the service, or rejects the request and denies access to the service. In some embodiments, the server sends a hash or other code which identifies the user and indicates the user has been validated/authorized by the server to the backend device, and in some embodiments, the server sends identification information and verification information to the backend device, and the backend device performs the verification/authentication. In some embodiments, fewer or additional steps are implemented. In some embodiments, the order of the steps is modified.

FIG. 17 illustrates a diagram of a system configured for implementing a method of an automated transparent login without saved credentials or passwords according to some embodiments. A device 1700 utilizes an authentication implementation (e.g., MFA) to ensure a confidence score of the user is above a threshold (e.g., the device is confident that the user is who he says he is). In some embodiments, the authentication information is based on the confidence score, and if the confidence score is above a threshold, no further information is needed, meaning the user does not need to enter login/password information or additional MFA information (e.g., satisfy a challenge). As described herein, the user's device with a confidence score above a threshold identifies the user as the correct user.

In some embodiments, MFA includes behavioral analytics, where the device continuously analyzes the user's behavior as described herein to determine a trust score for the user. The device (or system) determines a risk score for the user based on environmental factors such as where the device currently is, previous logins/locations, and more, and the risk score affects the user's confidence score. In some embodiments, the scan of a dynamic optical mark is only implemented if the user's trust score (or confidence score) is below a threshold. For example, if a user has been continuously using his device as he normally does, his gait matches the stored information, and his resulting trust score is 100 (out of 100) and there have been no anomalies with the user's device (e.g., the risk score is 0 out of 100), then there may be no need for further authentication/verification of the user.

In some embodiments, the authentication implementation utilizes additional MFA information. For example, for additional MFA information, the user utilizes the device's camera to scan a dynamic optical code/mark which is displayed on a secondary device 1702. In another example, a challenge requests the user to input a login and password for a site (e.g., a bank site).

After a user attempts to log in (e.g., clicks a link/button to log into a banking web page), the device 1700 sends a communication (e.g., an access/login request) via a quantum resistant encryption transport 1704 (or another transport) to a server device 1706. The server device 1706 then communicates the request/authentication information to a backend device 1708 (e.g., company device) which provides access to the desired services/information (e.g., log in to a web page with bank account information). Depending on the implementation, different information may be sent from the device 1700 to the server device 1706, and from the server device 1706 to the backend device 1708. For example, the device 1700 may send the acquired MFA information and/or a confidence score to the server device 1706. In another example, the server device 1706 may send a hash for access for a specific user login. The server device 1706 may send the login information and an associated request possibly accompanied by the confidence score. The server device 1706 may send any other data to trigger an access request for a specific user, including or not, an indication that the user should gain access to the backend service/device. The server device 1706 and the backend device 1708 are able to communicate in any manner, using any standard, and via any APIs.

The backend device 1708 is able to utilize standard login/access protocols such as OATH2, SAML, Kerberos and others. The backend device 1708 provides the login authorization (or not) back to the server device 1706 depending on the authentication information. The server device 1706 provides the authorization acceptance to the device 1700 enabling access to the web page. In some embodiments, the server device 1706 acts as a proxy server as described herein. In some embodiments, the server device 1706 performs the authentication verification and does not send the request to the backend device 1708 unless the authentication verification is determined to be true (e.g., user is verified as authentic). In some embodiments, the backend device 1708 communicates the authorization directly with the device 1700. In some embodiments, the implementation described herein is a single sign-on mechanism. By utilizing MFA as described herein, a user will no longer need to store login and password information in his browser.

In some embodiments, automated identification proofing using a random multitude of real-time behavioral biometric samplings is implemented. Single behavioral analysis is susceptible to hacking or spoofing with pre-recorded or eavesdropped data. For example, human speech may be recorded surreptitiously; or human motions (e.g., gait) may be recorded from a compromised personal device or hacked if stored on a central source. Using multiple behavioral biometric mechanisms, sampled randomly, is much more difficult to spoof. The larger number of biometric sensors and analytics employed greatly increases the security for authentication against either human hacking or robotic threats.

As described herein, Multi-Factor Authentication (MFA) is able to be based on possession factors, inheritance factors, and knowledge factors.

FIG. 18 illustrates a flowchart of a method of implementing automated identification proofing using a random multitude of real-time behavioral biometric samplings according to some embodiments. In the step 1800, a stack (or other structure) of MFA criteria is generated or modified. MFA information is able to be stored in a stack-type structure such that additional MFA criteria are able to be added to the stack. For example, initially, MFA analysis utilizes voice recognition, facial recognition, gait and typing style. Then, fingerprints and vein patterns are added to the stack so that more criteria are utilized for determining a trust score of a user. In some embodiments, a user selects the MFA criteria, and in some embodiments, a third party (e.g., phone maker such as Samsung, Apple, Google, or a software company or another company) selects the MFA criteria. The stack of MFA criteria is able to be modified by removing criteria. For example, if it has been determined that a user's fingerprint has been compromised, then that criterion may be removed and/or replaced with another criterion for that user.

In the step 1802, a random multitude of MFA information is analyzed. The MFA information is able to be based on: possession factors, inheritance factors, and knowledge factors. Possession factors are based on what the user possesses (e.g., key card, key FOB, credit/debit card, RFID, and personal smart devices such as smart phones, smart watches, smart jewelry, and other wearable devices). The personal smart devices are able to be used to perform additional tasks such as scanning/acquiring a dynamic optical mark/code using a camera. Inheritance factors are based on who the user is (e.g., biometrics such as fingerprints, hand scans, vein patterns, iris scans, facial scans, 3D facial scans, heart rhythm, and ear identification, and behavioral information such as voice tenor and patterns, gait, typing style, web page selection/usage). Knowledge factors are based on what a user knows (e.g., passwords, relatives' names, favorite image, previous addresses and so on).

Analysis of the MFA criteria is as described herein. For example, to analyze a user's gait, the user's gait information is stored, and the stored data points are compared with the current user's gait using motion analysis or video analysis. Similarly, a user's typing style is able to be captured initially during setup of the device, and then that typing style is compared with the current user's typing style. The analysis of the MFA criteria is able to occur at any time. For example, while the user is utilizing his device, the device may be analyzing his typing style or another criterion (possibly without the user knowing). Additionally, there are particular instances which trigger when the MFA criteria is analyzed, as described herein. For example, when it is detected that the device has left the user's possession, MFA analysis is performed upon device use resumption.

In some embodiments, the stack includes many criteria, but only some of the criteria are used in the analysis. For example, although 6 criteria are listed in a stack, the user has not provided a fingerprint, so that criterion is not checked when doing the analysis.

The MFA analysis is able to include challenges based on the trust score and/or an access request. Multiple thresholds are able to be implemented. For example, if a user's trust score is below 50%, then to perform any activities using the device, the user must solve a challenge (e.g., input a password, select a previously chosen favorite image, provide/answer another personal information question). Answering/selecting correctly boosts the user's trust score (the boost is able to be a percent increase or to a specific amount). In another example, if the user's trust score is above 50% but below 90%, the user is able to access lower priority applications/sites, but would be required to answer one or more challenges to raise the trust score above 90% to access high priority applications/sites such as a bank web site. In some embodiments, the trust score is part of a confidence score, and if the confidence score is below a threshold, then a challenge may be implemented.

In some embodiments, the analysis includes randomly sampling the MFA criteria. For example, although the MFA criteria stack may include eight criteria, each criterion is sampled in a random order. Furthering the example, when a user accesses his device, the user may be asked to provide a fingerprint, but then the next time he accesses his device, the user's gait is analyzed, and the next time, the user's typing style is analyzed, and so on. Any randomization is possible. In some embodiments, multiple criteria are analyzed together (e.g., typing style and fingerprints). In some embodiments, all of the criteria in a stack are utilized but are analyzed in a random fashion/order. For example, when a user accesses a device, he is required to input a password/PIN, then while the user is typing, his typing style is analyzed, and while the user is walking his gait is analyzed, but if the user starts typing again, his typing style is analyzed, and every once in a while a retina scan is requested/performed. The analysis of the criteria is able to be performed in any random order. In another example, sometimes when a user attempts to gain access to a device, he is prompted to provide a fingerprint, other times a password or PIN is requested, and sometimes a retinal scan is implemented. By changing the criteria being analyzed, even if a hacker has the user's password, if the hacker does not have the user's fingerprint or retina scan, their attempt to gain access will be thwarted. As described herein, in some embodiments, multiple criteria are utilized in combination at the same time or at different times.

In the step 1804, a user's trust score is adjusted based on the analysis of the MFA information. As described herein, the user's trust score goes up, down or stays the same based on the MFA information analysis. For example, if a current user's gait matches the stored information of the correct user's gait, then the user's trust score goes up (e.g., is increased). If the current user's typing style is different than the stored information of the correct user, then the user's trust score goes down (e.g., is decreased).

The amount that the trust score is adjusted is able to depend on the implementation. In some embodiments, the effect on the user's trust score is able to be absolute or proportional. For example, in some embodiments, if one criterion out of eight criteria is not a match, then the user's trust score drops significantly (e.g., by 50% or to 0). In another example, in some embodiments, if one criterion of eight is missed, then the trust score drops proportionately (e.g., by ⅛^(th)). In another example, the amount of the drop may depend on how close the currently acquired information is when compared to the stored information. For example, using comparative analysis, a user's gait is a 97% match with the stored information, so the trust score may drop slightly or not at all since the match is very close, whereas a match of 50% may cause a significant drop in the trust score (e.g., by 50% or another amount). When utilizing MFA criteria, if a user's current analysis results in a mismatch (e.g., the user has a different gait), then the user's trust score is lowered, even if the other criteria are matches. For example, seven of eight criteria are matches, but one of the criterion is a mismatch. In some embodiments, one mismatch significantly affects the user's trust score, and in some embodiments, the device/system is able to account for the fact that seven of eight criteria were matches, so the drop in the trust score may be minimal or proportionate. For example, one mismatch out of seven reduces the trust score by less than one mismatch out of two. In some embodiments, if there is one mismatch out of many criteria, the user may be prompted as to why there was a mismatch (e.g., an injury could cause the user to change his gait), and/or another criterion may be utilized.

As described herein, the trust score of the user for a device is able to be used as part of a confidence score (e.g., the confidence score is based on the trust score and a risk score). The confidence score is then used to determine whether the device or system has confidence that the user is who he says he is and what applications/sites the user has access to. A mismatch in the analysis criteria affects the confidence score, and based on the confidence score, additional factors/criteria may be analyzed and/or additional challenges may be utilized. In some embodiments, fewer or additional steps are implemented. In some embodiments, the order of the steps is modified.

In some embodiments, user identification proofing is implemented using a combination of user responses to system Turing tests using biometric methods. For example, device and/or system determines if the user is the correct user (e.g., the user is who he says he is) and is the user a human (and not a bot).

FIG. 19 illustrates a flowchart of a method of implementing user identification proofing using a combination of user responses to system Turing tests using biometric methods according to some embodiments.

In the step 1900, biometric/behavioral analysis is performed. Biometric analysis is able to be implemented as described herein and include analyzing: fingerprints, hand scans, vein patterns, iris scans, facial scans, 3D facial scans, heart rhythm, ear identification and others, and behavioral analysis is able to include analysis of information such as voice tenor and patterns, gait, typing style, web page selection/usage and others. For example, the device utilizes sensors, cameras, and/or other devices/information to scan/acquire/capture biometric and/or behavioral information for/from the user. The biometric/behavioral analysis is able to include comparing acquired information (e.g., fingerprints) with stored information (e.g., previously acquired fingerprints) and determining how close the information is and whether there is a match. Any implementation of comparison/matching is able to be implemented.

In the step 1902, a biometric/behavioral challenge/Turing test is implemented. For example, a user is requested to turn his head a certain direction or look a certain direction. Furthering the example, the user is prompted by the device to look up and then look right, and the camera of the device captures the user's motions and analyzes the user's motions using video processing implementations to determine if the user looked in the correct directions. In another example, voice recognition is able to be implemented including asking a user to repeat a specific, random phrase (e.g., a random set of word combinations such as “kangaroo, hopscotch, automobile”). The vocal fingerprint and the pattern of how a user talks are able to be analyzed. For example, the device/system is able to detect computer synthesized phrases by detecting changes in pitch, odd gaps (or a lack of gaps) between words, and other noticeable distinctions. Other actions are able to be requested and analyzed as well such as requesting the user to skip, jump, walk a certain way, and so on.

In some embodiments, the biometric/behavioral challenge/Turing test is related to the biometric/behavioral analysis (e.g., in the same class/classification). For example, if the biometric/behavioral test involves facial recognition, then then the biometric/behavioral challenge/Turing test is related to facial recognition such as requesting the user to turn his head in one or more specific directions. In some embodiments, the challenge/test is unrelated to the biometric/behavioral analysis (e.g., in a different class/classification). For example, if there is a concern that a user's facial recognition information has been compromised (e.g., detection of the same facial information within a few minutes in two different parts of the world), then the challenge/test is something unrelated to that specific biometric/behavioral analysis. Furthering the example, instead of asking the user to look a specific direction, the user is requested to speak a randomly generated phrase/sequence of words or to perform an action (e.g., jump, specific exercise). Exemplary classes/classifications include a facial/head class, a gait class, a speech/voice class, a typing class, and others.

The device utilizes sensors, cameras, and/or other devices/information to scan/acquire/capture biometric and/or behavioral information for/from the user to perform the challenge/Turing test. For example, the sensors/cameras capture user information and compare the user information with stored user information to determine if there is a match. In some embodiments, computer learning is able to be implemented to perform the analysis. For example, using computer learning, the analysis/matching is able to be implemented on possible iterations that were not specifically captured but are able to be estimated or extrapolated based on the captured information. In some embodiments, the challenge/Turing test is only implemented if the user passes the biometric/behavioral analysis. In some embodiments, the device (e.g., mobile phone) implements the analysis and challenge/test steps, and in some embodiments, one or more of the steps (or part of the steps) are implemented on a server device. For example, the device acquires the biometric and/or behavioral information which is sent to a server device to perform the analysis of the acquired biometric/behavioral information. Similarly, a response by a user to the challenge/Turing test is able to be acquired by a user device, but the acquired information is able to be analyzed on the server device.

In some embodiments, fewer or additional steps are implemented. For example, after a user is verified using the analysis and challenge/Turing test, the user is able to access the device and/or specific apps/sites using the device. In another example, after a user is verified using the analysis and challenge/Turing test, the trust score, and in conjunction, the confidence score of the user increases. In some embodiments, the order of the steps is modified.

Any of the implementations described herein are able to be used with any of the other implementations described herein. In some embodiments, the implementations described herein are implemented on a single device (e.g., user device, server, cloud device, backend device) and in some embodiments, the implementations are distributed across multiple devices, or a combination thereof.

The present invention has been described in terms of specific embodiments incorporating details to facilitate the understanding of principles of construction and operation of the invention. Such reference herein to specific embodiments and details thereof is not intended to limit the scope of the claims appended hereto. It will be readily apparent to one skilled in the art that other various modifications may be made in the embodiment chosen for illustration without departing from the spirit and scope of the invention as defined by the claims. 

What is claimed is:
 1. A method comprising: detecting, with a device, that the device has left possession of a user by detecting that the device has been set down, wherein detecting that the device has been set down includes utilizing artificial intelligence to learn patterns of setting the device down, wherein the patterns are used to compare and match stored information to determine a set down motion; lowering a trust score of the user of the device upon detecting that the device has left possession of the user; and implementing, with the device, a challenge to verify the user.
 2. The method of claim 1 wherein detecting that the device has been set down includes: utilizing a sensor to detect that the device is stationary; utilizing a proximity sensor to detect that the device is proximate to another object; utilizing a sensor to detect that the device is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the device has been determined to have been set down.
 3. The method of claim 2 wherein a camera of the device is used as the proximity sensor.
 4. The method of claim 1 wherein detecting that the device has been set down includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down.
 5. The method of claim 1 wherein detecting that the device has left possession of the user is by detecting that the device has been handed off.
 6. The method of claim 5 wherein detecting that the device has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 7. The method of claim 5 wherein detecting that the device has been handed off includes utilizing artificial intelligence to learn patterns of handing off the device, wherein the patterns are used to compare and match stored information to determine a hand off motion.
 8. The method of claim 1 wherein detecting that the device has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 9. The method of claim 1 wherein the trust score lowers to zero upon detecting that the device has left possession of the user.
 10. The method of claim 1 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 11. The method of claim 1 further comprising performing behavioral analytics after implementing the challenge to further verify the user.
 12. An apparatus comprising: a memory for storing an application, the application configured for: detecting that the apparatus has left possession of a user by detecting that the apparatus has been set down, wherein detecting that the device has been set down includes utilizing artificial intelligence to learn patterns of setting the device down, wherein the patterns are used to compare and match stored information to determine a set down motion; lowering a trust score of the user of the apparatus upon detecting that the apparatus has left possession of the user; and implementing a challenge to verify the user; and a processor configured for processing the application.
 13. The apparatus of claim 12 wherein detecting that the apparatus has been set down includes: utilizing a sensor to detect that the apparatus is stationary; utilizing a proximity sensor to detect that the apparatus is proximate to another object; utilizing a sensor to detect that the apparatus is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the apparatus has been determined to have been set down.
 14. The apparatus of claim 13 wherein a camera of the apparatus is used as the proximity sensor.
 15. The apparatus of claim 12 wherein detecting that the apparatus has been set down includes analyzing and categorizing separate components of a process of setting the apparatus down, and comparing each separate component with stored information to make a determination of whether the apparatus is set down.
 16. The apparatus of claim 12 wherein detecting that the apparatus has left possession of the user is by detecting that the apparatus has been handed off.
 17. The apparatus of claim 16 wherein detecting that the apparatus has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 18. The apparatus of claim 16 wherein detecting that the apparatus has been handed off includes utilizing artificial intelligence to learn patterns of handing off the apparatus, wherein the patterns are used to compare and match stored information to determine a hand off motion.
 19. The apparatus of claim 12 wherein detecting that the apparatus has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 20. The apparatus of claim 12 wherein the trust score lowers to zero upon detecting that the apparatus has left possession of the user.
 21. The apparatus of claim 12 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 22. The apparatus of claim 12 wherein the application is further configured for performing behavioral analytics after implementing the challenge to further verify the user.
 23. A method comprising: detecting, with a device, setting down of the device, wherein detecting setting down the device includes utilizing artificial intelligence to learn patterns of setting the device down, wherein the patterns are used to compare and match stored information to determine a set down motion; detecting, with the device, a hand off of the device; lowering a trust score of the user of the device upon detecting setting down of the device or the hand off of the device; locking the device upon detecting setting down of the device or the hand off of the device; and implementing, with the device, a challenge to verify the user and unlock the device.
 24. The method of claim 23 wherein detecting setting down the device includes: utilizing a sensor to detect that the device is stationary; utilizing a proximity sensor to detect that the device is proximate to another object; utilizing a sensor to detect that the device is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the device has been determined to have been set down.
 25. The method of claim 24 wherein a camera of the device is used as the proximity sensor.
 26. The method of claim 24 wherein detecting setting down the device includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down.
 27. The method of claim 24 wherein detecting the hand off of the device includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 28. The method of claim 24 wherein detecting the hand off of the device includes utilizing artificial intelligence to learn patterns of handing off the device, wherein the patterns are used to compare and match stored information to determine a hand off motion.
 29. The method of claim 24 wherein detecting setting down the device and the hand off of the device includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 30. The method of claim 24 wherein the trust score lowers to zero upon detecting setting down the device and the hand off of the device.
 31. The method of claim 24 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 32. The method of claim 24 further comprising performing behavioral analytics after implementing the challenge to further verify the user.
 33. A method comprising: detecting, with a device, that the device has left possession of a user by detecting that the device has been set down, wherein detecting that the device has been set down includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down; lowering a trust score of the user of the device upon detecting that the device has left possession of the user; and implementing, with the device, a challenge to verify the user.
 34. The method of claim 33 wherein detecting that the device has been set down includes: utilizing a sensor to detect that the device is stationary; utilizing a proximity sensor to detect that the device is proximate to another object; utilizing a sensor to detect that the device is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the device has been determined to have been set down.
 35. The method of claim 34 wherein a camera of the device is used as the proximity sensor.
 36. The method of claim 33 wherein detecting that the device has left possession of the user is by detecting that the device has been handed off.
 37. The method of claim 36 wherein detecting that the device has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 38. The method of claim 36 wherein detecting that the device has been handed off includes utilizing artificial intelligence to learn patterns of handing off the device, wherein the patterns are used to compare and match stored information to determine a hand off motion.
 39. The method of claim 33 wherein detecting that the device has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 40. The method of claim 33 wherein the trust score lowers to zero upon detecting that the device has left possession of the user.
 41. The method of claim 33 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 42. The method of claim 33 further comprising performing behavioral analytics after implementing the challenge to further verify the user.
 43. An apparatus comprising: a memory for storing an application, the application configured for: detecting that the apparatus has left possession of a user by detecting that the apparatus has been set down, wherein detecting that the device has been set down includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down; lowering a trust score of the user of the apparatus upon detecting that the apparatus has left possession of the user; and implementing a challenge to verify the user; and a processor configured for processing the application.
 44. The apparatus of claim 43 wherein detecting that the apparatus has been set down includes: utilizing a sensor to detect that the apparatus is stationary; utilizing a proximity sensor to detect that the apparatus is proximate to another object; utilizing a sensor to detect that the apparatus is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the apparatus has been determined to have been set down.
 45. The apparatus of claim 44 wherein a camera of the apparatus is used as the proximity sensor.
 46. The apparatus of claim 43 wherein detecting that the apparatus has left possession of the user is by detecting that the apparatus has been handed off.
 47. The apparatus of claim 46 wherein detecting that the apparatus has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 48. The apparatus of claim 46 wherein detecting that the apparatus has been handed off includes utilizing artificial intelligence to learn patterns of handing off the apparatus, wherein the patterns are used to compare and match stored information to determine a hand off motion.
 49. The apparatus of claim 43 wherein detecting that the apparatus has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 50. The apparatus of claim 43 wherein the trust score lowers to zero upon detecting that the apparatus has left possession of the user.
 51. The apparatus of claim 43 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 52. The apparatus of claim 43 wherein the application is further configured for performing behavioral analytics after implementing the challenge to further verify the user.
 53. A method comprising: detecting, with a device, that the device has left possession of a user by detecting that the device has been handed off, wherein detecting that the device has been handed off includes utilizing artificial intelligence to learn patterns of handing off the device, wherein the patterns are used to compare and match stored information to determine a hand off motion; lowering a trust score of the user of the device upon detecting that the device has left possession of the user; and implementing, with the device, a challenge to verify the user.
 54. The method of claim 53 wherein detecting that the device has been set down includes: utilizing a sensor to detect that the device is stationary; utilizing a proximity sensor to detect that the device is proximate to another object; utilizing a sensor to detect that the device is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the device has been determined to have been set down.
 55. The method of claim 54 wherein a camera of the device is used as the proximity sensor.
 56. The method of claim 53 wherein detecting that the device has left possession of the user is by detecting that the device has been set down, wherein detecting that the device has been set down includes utilizing artificial intelligence to learn patterns of setting the device down, wherein the patterns are used to compare and match stored information to determine a set down motion.
 57. The method of claim 53 wherein detecting that the device has left possession of the user is by detecting that the device has been set down, wherein detecting that the device has been set down includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down.
 58. The method of claim 53 wherein detecting that the device has left possession of the user is by detecting that the device has been handed off.
 59. The method of claim 58 wherein detecting that the device has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 60. The method of claim 53 wherein detecting that the device has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 61. The method of claim 53 wherein the trust score lowers to zero upon detecting that the device has left possession of the user.
 62. The method of claim 53 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 63. The method of claim 53 further comprising performing behavioral analytics after implementing the challenge to further verify the user.
 64. An apparatus comprising: a memory for storing an application, the application configured for: detecting that the apparatus has left possession of a user by detecting that the device has been handed off, wherein detecting that the device has been handed off includes utilizing artificial intelligence to learn patterns of handing off the device, wherein the patterns are used to compare and match stored information to determine a hand off motion; lowering a trust score of the user of the apparatus upon detecting that the apparatus has left possession of the user; and implementing a challenge to verify the user; and a processor configured for processing the application.
 65. The apparatus of claim 64 wherein detecting that the apparatus has been set down includes: utilizing a sensor to detect that the apparatus is stationary; utilizing a proximity sensor to detect that the apparatus is proximate to another object; utilizing a sensor to detect that the apparatus is in a horizontal orientation; and/or utilizing a microphone to determine a distance of a voice of the user, wherein when the distance of the voice of the user is above a threshold, then the apparatus has been determined to have been set down.
 66. The apparatus of claim 65 wherein a camera of the apparatus is used as the proximity sensor.
 67. The apparatus of claim 64 wherein detecting that the device has left possession of the user is by detecting that the device has been set down, wherein detecting that the device has been set down includes utilizing artificial intelligence to learn patterns of setting the device down, wherein the patterns are used to compare and match stored information to determine a set down motion.
 68. The apparatus of claim 64 wherein detecting that the device has left possession of the user is by detecting that the device has been set down, wherein detecting that the device has been set down includes analyzing and categorizing separate components of a process of setting the device down, and comparing each separate component with stored information to make a determination of whether the device is set down.
 69. The apparatus of claim 64 wherein detecting that the apparatus has left possession of the user is by detecting that the apparatus has been handed off.
 70. The apparatus of claim 69 wherein detecting that the apparatus has been handed off includes: utilizing a sensor to detect multiple points of contact; and/or utilizing a sensor to recognize a handoff motion.
 71. The apparatus of claim 64 wherein detecting that the apparatus has left possession of the user includes analyzing movement, proximity, sound, scanning/video, shaking, touch, pressure, and/or orientation using a camera, a screen, a microphone, an accelerometer, a gyroscope and/or a sensor.
 72. The apparatus of claim 64 wherein the trust score lowers to zero upon detecting that the apparatus has left possession of the user.
 73. The apparatus of claim 64 wherein implementing the challenge to verify the user includes utilizing biometrics, providing a password request, performing a question challenge, requesting a favorite image selection, performing facial recognition, performing 3D facial recognition and/or performing voice recognition.
 74. The apparatus of claim 64 wherein the application is further configured for performing behavioral analytics after implementing the challenge to further verify the user. 